Open AccessPowerNet: a smart energy forecasting architecture based on neural networks
Author(s) -
Cheng Yao,
Xu Chang,
Mashima Daisuke,
Biswas Partha P.,
Chipurupalli Geetanjali,
Zhou Bin,
Wu Yongdong
Publication year - 2020
Publication title -
iet smart cities
Language(s) - English
Resource type - Journals
ISSN - 2631-7680
DOI - 10.1049/iet-smc.2020.0003
Subject(s) - computer science , gradient boosting , smart grid , smart meter , support vector machine , boosting (machine learning) , demand forecasting , anomaly detection , random forest , decision tree , artificial neural network , electricity , ensemble learning , grid , tree (set theory) , machine learning , artificial intelligence , operations research , engineering , mathematics , mathematical analysis , geometry , electrical engineering
Electricity demand forecasting is a critical task for efficient, reliable and economical operation of the power grid, which is one of the most essential building blocks of smart cities. Accurate forecasting allows grid operators to properly maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet which can incorporate multiple heterogeneous features such as historical energy consumption data, weather data and calendar information for the demand forecasting task. Using real‐world smart meter dataset, we conduct an extensive evaluation to show the advantages of PowerNet over recently‐proposed machine learning methods such as Gradient Boosting Tree (GBT), Support Vector Regression (SVR), Random Forest (RF) and Gated Recurrent Unit (GRU). PowerNet demonstrates notable performance in reducing both the median and worst‐case prediction errors when forecasting demands of individual residential households. We further provide empirical results concerning the two operational considerations that are crucial when using PowerNet in practice: the time horizon the model can predict with a decent accuracy and the frequency of training the model to retain its modeling capability. Finally, we briefly discuss a multi‐layer anomaly/electricity‐theft detection approach based on PowerNet demand forecasting.